{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "619f5ac8-a897-42b0-be8d-824761fa954c",
   "metadata": {},
   "source": [
    "# 民宿价格数据分析\n",
    "探究各个因素对总价Price的影响，特征值之间的强相关性\n",
    "使用4种机器学习模型对数据集进行价格预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7970d857-4338-402f-90b4-1e28236f69e2",
   "metadata": {},
   "source": [
    "## 一、获取数据\n",
    "数据的获取是从木鸟网上爬取的民宿信息"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5c943a-c001-437f-aa96-e9a644916b2f",
   "metadata": {},
   "source": [
    "## 二、数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d63247-794f-4c12-ae75-527da3f53c89",
   "metadata": {},
   "source": [
    "### （1）导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "497229ce-e2da-4435-a1c4-9f082b4ed6f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1674 entries, 0 to 1673\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   room_id        1674 non-null   int64 \n",
      " 1   room_title     1674 non-null   object\n",
      " 2   room_address   1674 non-null   object\n",
      " 3   hx             1674 non-null   object\n",
      " 4   cz             1674 non-null   object\n",
      " 5   bed            1674 non-null   object\n",
      " 6   people         1674 non-null   object\n",
      " 7   area           1674 non-null   object\n",
      " 8   applause_rate  1674 non-null   object\n",
      " 9   room_price     1674 non-null   int64 \n",
      "dtypes: int64(2), object(8)\n",
      "memory usage: 130.9+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('homestay.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43c887f-9b3f-4daf-84a8-e26d894d1375",
   "metadata": {},
   "source": [
    "### （2）重复值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be8eede0-e9f4-49e0-a3f3-002fefee5a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1669    False\n",
       "1670    False\n",
       "1671    False\n",
       "1672    False\n",
       "1673    False\n",
       "Length: 1674, dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dae23c8-a809-4862-80b8-7b369af55ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()  # 统计重复数据的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "978f2874-8064-4e76-8b38-cc5e21e47853",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)  # 直接丢弃重复数据\n",
    "df.reset_index(drop=True, inplace=True)  # 因为丢弃了数据，所以索引不连续，需要重新设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a20afc12-297d-441c-8d95-f2f8e7b7252a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()  # 查看是否还有重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92a4c366-d8f0-40fa-98e5-db0cccc369d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1354, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  # 查看有用数据总数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7571033e-8716-4bfe-8008-89ca9aae99ff",
   "metadata": {},
   "source": [
    "### （3）缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78d35801-e5b3-4a09-a26e-6472768be699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_title</th>\n",
       "      <th>room_address</th>\n",
       "      <th>hx</th>\n",
       "      <th>cz</th>\n",
       "      <th>bed</th>\n",
       "      <th>people</th>\n",
       "      <th>area</th>\n",
       "      <th>applause_rate</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>1349</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>1352</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1354 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      room_id  room_title  room_address     hx     cz    bed  people   area  \\\n",
       "0       False       False         False  False  False  False   False  False   \n",
       "1       False       False         False  False  False  False   False  False   \n",
       "2       False       False         False  False  False  False   False  False   \n",
       "3       False       False         False  False  False  False   False  False   \n",
       "4       False       False         False  False  False  False   False  False   \n",
       "...       ...         ...           ...    ...    ...    ...     ...    ...   \n",
       "1349    False       False         False  False  False  False   False  False   \n",
       "1350    False       False         False  False  False  False   False  False   \n",
       "1351    False       False         False  False  False  False   False  False   \n",
       "1352    False       False         False  False  False  False   False  False   \n",
       "1353    False       False         False  False  False  False   False  False   \n",
       "\n",
       "      applause_rate  room_price  \n",
       "0             False       False  \n",
       "1             False       False  \n",
       "2             False       False  \n",
       "3             False       False  \n",
       "4             False       False  \n",
       "...             ...         ...  \n",
       "1349          False       False  \n",
       "1350          False       False  \n",
       "1351          False       False  \n",
       "1352          False       False  \n",
       "1353          False       False  \n",
       "\n",
       "[1354 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()  # 查看某个字段是否为空"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0fdd2c0-5e54-47f6-b1a3-d47d95471594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_id          0\n",
       "room_title       0\n",
       "room_address     0\n",
       "hx               0\n",
       "cz               0\n",
       "bed              0\n",
       "people           0\n",
       "area             0\n",
       "applause_rate    0\n",
       "room_price       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  # 按axis=0行变化的方向求和统计到底哪些列有缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07dc2c0a-18d2-43fa-a02d-fc3837409b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_id          0\n",
       "room_title       0\n",
       "room_address     0\n",
       "hx               0\n",
       "cz               0\n",
       "bed              0\n",
       "people           0\n",
       "area             0\n",
       "applause_rate    0\n",
       "room_price       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sort_values(ascending=False)  # 按照值进行降序排序，将最多缺失值的列放在最上面展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1134c688-deae-490e-a116-da79f5476126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_id          0.0\n",
       "room_title       0.0\n",
       "room_address     0.0\n",
       "hx               0.0\n",
       "cz               0.0\n",
       "bed              0.0\n",
       "people           0.0\n",
       "area             0.0\n",
       "applause_rate    0.0\n",
       "room_price       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_missing = df.isnull().sum().sort_values(ascending=False)\n",
    "# 计算缺失值的在总数据中的占比\n",
    "percent = (df.isnull().sum() / len(df)).sort_values(ascending=False).round(3)\n",
    "percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87c86828-949e-48f7-9897-642e9c415dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>room_title</th>\n",
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       "      <th>room_address</th>\n",
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       "      <th>hx</th>\n",
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       "    <tr>\n",
       "      <th>cz</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>bed</th>\n",
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       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>applause_rate</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total  Percent\n",
       "room_id            0      0.0\n",
       "room_title         0      0.0\n",
       "room_address       0      0.0\n",
       "hx                 0      0.0\n",
       "cz                 0      0.0\n",
       "bed                0      0.0\n",
       "people             0      0.0\n",
       "area               0      0.0\n",
       "applause_rate      0      0.0\n",
       "room_price         0      0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data = pd.concat([total_missing, percent],\n",
    "                         axis=1, keys=['Total', 'Percent'])\n",
    "missing_data  # 查看缺失值的个数和占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50b7985c-7958-4a1a-bcf4-dd0b35ef24ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_address\n",
       "大理-大理市-机场路-怡景尚居                    45\n",
       "大理-大理市-机场路-万科拾叁月                   19\n",
       "大理-大理市-机场路-洱海寰球时代                  18\n",
       "大理-大理市-县道Z01东-洱海环球时代               13\n",
       "大理-大理市-太和街道大井盘村                    12\n",
       "                                   ..\n",
       "大理-大理市-海云居西南侧-小院子北区                 1\n",
       "大理-大理市-机场路洱海国际生态城东北侧约170米-万科拾叁月     1\n",
       "大理-大理市-海云居南侧约260米-大理的小院子北区          1\n",
       "大理-大理市-环海西路-古生村                     1\n",
       "大理-大理市-大理古城北门街20号北门古楼100米           1\n",
       "Name: count, Length: 711, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于上述统计可知只有room_address\n",
    "df[\"room_address\"].fillna(\"未知\").value_counts()  # 给room_address字段缺失的地方填充“未知”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f0da5c1-e509-4f38-8417-9cc66bfc1442",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据各特征的现实意义，填充合适的值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090c3a7e-4620-4364-bda7-7a4096a3ce48",
   "metadata": {},
   "source": [
    "### （4）异常值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e9e7e82-ee65-4dd4-9eb8-0f389dc33b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 发现离群点\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb1b18f3-82f2-4518-8eec-71aa1cb6204d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用正则表达式提取数字\n",
    "df['area'] = df['area'].str.extract(r'(\\d+)').astype(float)\n",
    "df['people'] = df['people'].str.extract(r'(\\d+)').astype(int)\n",
    "df['hx'] = df['hx'].str.extract(r'(\\d+)').astype(int)\n",
    "df['bed'] = df['bed'].str.extract(r'(\\d+)').astype(int)\n",
    "df['applause_rate'] = df['applause_rate'].str.extract(r'(\\d+)').astype(float)\n",
    "df['cz'] = df['cz'].map({'整租': 1, '单间': 0}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cdcaa165-d753-4c9d-8ede-24105f236096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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991wjJSXFOP/8841Vq1b59FrsdrshybDb7b5fCAAAAIScL3kt6vs8xzr6PAMAAES3DtHnGQAAAAg3wjMAAABgEuEZAAAAMInwDAAAAJhEeAYAAABMIjwDAAAAJhGeAQAAAJMIzwAAAIBJhGcAAADAJMIzAAAAYBLhGQAAADCJ8AwAAACYRHgGAAAATCI8AwAAACYRngEAAACTCM8AAACASYRnAAAAwCTCMwAAAGAS4RkAAAAwifAMAAAAmER4BgAAAExKivQEAABA8DQ5DJWUV+lQbZ1yuqRpWH6WEhMskZ4WEDcIzwAAxImi0go9vKJMFfY612O51jTNHVug0f1zIzgzIH5QtgEAQBwoKq3QtKVb3YKzJFXa6zRt6VYVlVZEaGZAfCE8AwAQ45ochh5eUSajjeecjz28okxNjrZGAPAF4RkAgBhXUl7VasW5OUNShb1OJeVV4ZsUEKcIzwAAxLhDtZ6Dsz/jAHhGeAYAIMbldEkL6jgAntFtAwCCjFZhCLdh+VnKtaap0l7XZt2zRZLNeurvIoDAEJ4BIIhoFYZISEywaO7YAk1bulUWyS1AO9+2zR1bwJs4IAgo2wCAIPGnVViTw1Dx3sNavv1LFe89TDcE+G10/1wtvG2wbFb30gybNU0LbxvMmzcgSFh5BoAgaK9VmEWnWoVdVWBzrf6xSo1gG90/V1cV2CgbAkKIlWcACAJfW4WxoQVCJTHBosI+2bph0Bkq7JNNcAaCjPAMAEHgS6swNrQAgNhFeAaAIPClVRgbWgBA7CI8A0AQOFuFefqA3KJT9czD8rPY0AIAYhjhGQD+I5DOF85WYZJaBeiWrcLY0AIAYhfdNgBAwel84WwV1vI4thbHYUMLAIhdFsMwuCMlhGpqamS1WmW325WRkRHp6QBog7PzRcv/GTpXjH3tkWtmh0HnOaW2N7SgLy8AhI8veY2yDQAdWig6X5hpFcaGFgAQmyjbANCh+dL5orBPdlDPzYYWABB7CM8AOrRId75wrlIDAGIDZRsAOjQ6XwAAfEF4BtCh+dKfGQAAwjOADs2X/swAAAQtPNvtdr311lt64YUXtHHjxmAdFgBCjs4X0S+QDWwAIJgCvmGwtrZWs2bN0t/+9jc1NjZKkm6//XZ961vfkiT9+c9/1kMPPaTXXntNw4cPD/R0ABASdL6IXsHYwAYAgiWglecTJ07o29/+tv7yl7+oa9euuuaaa9Ryz5XrrrtOBw8e1LJlywI5FQCEnJn+zAgv52YyLdsJVtrrNG3pVhWVVkRoZgA6qoDC8//8z/9o27ZtGj9+vPbu3auVK1e2GmOz2dSvXz+9/fbbgZwKANDBhGIDGwAIVEDh+aWXXpLNZtOzzz6rzp07exx37rnn6osvvgjkVEBQUT8JRD9fNrABgHAJqOZ57969uuqqq5SW5r3/aXp6ur7++utATgUEDfWTQGyI9AY2ANCWgFaeExMT1dDQ0O64L774wuvKNBAu1E8CsYMNbABEo4DCc58+ffThhx+6umy05ejRo/roo4/Ur1+/QE4FBIz6SSC2sIENgGgUUHi+/vrrVVFRoV/96lcex/zqV7+S3W7XjTfeGMipgIBRPwnEFjawARCNAgrPs2bN0hlnnKFHHnlE48aN0/PPPy9JOnjwoF599VXdeuutevzxx9W7d29NnTo1KBMG/EX9JBB72MAGQLSxGC0bM/uorKxM119/vT799FNZLO7v/g3DUK9evbRq1SoVFBQENNFYVVNTI6vVKrvdroyMjEhPp0Mr3ntY4xdvanfcC1NGqLBPdhhmBMCsJofBBjYAQsaXvBbwDoMFBQUqLS3VkiVL9MYbb+jTTz+Vw+FQXl6errnmGt15551KT08P9DRAwJz1k5X2ujbrni06tZpF/SQQfZwb2ABApAW88gzvWHmOLs5uG5LcArRz/YqPgQEA6Hh8yWsB1TwDsYb6SQAAEIiAyjZ27Nihf/zjHxo7dqwuvPDCNsds3bpVK1eu1Pe+9z317ds3kNMhysVKTeLo/rm6qsAWE3MFAADRJaDw/Mc//lF/+tOfNGnSJI9junXrpocfflhVVVV64oknAjkdolis7dpH/SQAAPBHQGUb77zzji644ALl5eV5HNOzZ08NHDhQa9asCeRUiGLs2gcAADqKgMLzF198obPOOqvdcWeddZa+/PLLQE6FKMWufQAAoCMJKDw3NjYqIaH9QyQkJKiujo0n4hG79gEAgI4koPCcl5en999/v91x77//vnr06BHIqRCl2LUPAAB0JAGF5yuuuEL79+/XH//4R49jFi5cqM8++0xXXHFFIKdClMrpktb+IB/GAQAARLOAwvOsWbOUkpKin/70p5o1a5bKysrU1NSkpqYmlZWVadasWfrpT3+qlJQUzZ49O1hzRhRx7trnqcmbRae6brBrHwAAiAcB7zD4/PPPa9KkSWpsbGz1nGEYSkpK0rPPPquJEycGcpqY1RF2GGTXPgAAEMvCusPg97//fRUXF+v6669Xenq6DMOQYRjq1KmTbrjhBm3cuLHDBueOgl37AABARxHwynNzDodDhw8fliRlZ2eb6sQR7zrCyrNTrOwwCAAA0JwveS2gHQZbSkhI0Omnnx7MQyKGsGsfAACIdywNAwAAACb5tPL8y1/+UpI0Y8YMZWVlub43w2Kx6MEHH/RtdgAAAEAU8anmOSEhQRaLRTt37tS5557r+t7bIZzPWywWNTU1BWXSsaQj1TwDAADEopDVPD/00EOyWCzq1q2b2/cAAABARxDUbhtojZVnAACA6Ba2Ps/r1q3Thg0bAjkEAAAAEDMCCs/f/va3uQkQAAAAHUZA4blr167q0aNHsOYCAAAARLWAwvOgQYO0Z8+eYM0FAAAAiGoBheef/vSnev/997Vq1apgzQcAAACIWgFtz33hhRdqxowZuvHGG3XHHXfo5ptvVu/evdWpU6c2x/fs2TOQ0wEAAAARFVCrusTERElybYLi9UQWixobG/09VcyiVR0AAEB0C9kmKS3l5eWxSQrU5DBUUl6lQ7V1yumSpmH5WUpM4O8FAACIPwHVPO/bt0/l5eWmv8xat26dxo4dqx49eshisWjZsmVuzxuGoYceeki5ubnq1KmTRo4c2erGxaqqKk2YMEEZGRnKzMzU5MmTdfToUbcxH330kS699FKlpaUpLy9Pjz32WKu5vPLKK+rbt6/S0tI0YMAAvfHGG+YvUAdQVFqhSx5dq/GLN+nuF7dr/OJNuuTRtSoqrYj01AAAAIIuoPAcKseOHdPAgQP19NNPt/n8Y489pj/84Q9atGiRNm/erM6dO2vUqFGqq6tzjZkwYYJ27Nih1atXa+XKlVq3bp3uvPNO1/M1NTW6+uqr1atXL23ZskWPP/645s2bp2eeecY1ZuPGjRo/frwmT56sbdu2ady4cRo3bpxKS0tD9+JjSFFphaYt3aoKe53b45X2Ok1bupUADQAA4k7Ub89tsVj02muvady4cZJOrTr36NFD99xzj372s59Jkux2u7p3764lS5bo1ltv1c6dO1VQUKD3339fQ4cOlSQVFRXp2muv1RdffKEePXpo4cKF+vnPf67KykqlpKRIku6//34tW7ZMu3btkiTdcsstOnbsmFauXOmaz4gRIzRo0CAtWrTI1Pzjtea5yWHokkfXtgrOThZJNmua3rvvCko4AABAVAvb9txOX3/9tRYsWKDRo0erf//+Ov/88zVq1CgtWLBAhw4dCsYpXMrLy1VZWamRI0e6HrNarRo+fLiKi4slScXFxcrMzHQFZ0kaOXKkEhIStHnzZteYyy67zBWcJWnUqFHavXu3jhw54hrT/DzOMc7zdGQl5VUeg7MkGZIq7HUqKa8K36SAKNHkMFS897CWb/9SxXsPq8kR1WsUAAAfBHTDoCQtX75ckyZNkt1uV/NF7J07d+qtt97SggUL9Nxzz+nGG28M9FSSpMrKSklS9+7d3R7v3r2767nKykrl5OS4PZ+UlKSsrCy3Mfn5+a2O4Xyua9euqqys9Hqetpw8eVInT550fV9TU+PLy4sZh2o9B2d/xgHxoqi0Qg+vKHN7c5lrTdPcsQUa3T83gjMDAARDQCvPmzdv1n/913+purpaQ4YM0R/+8AetWLFCK1as0JNPPqmhQ4eqpqZGt9xyi2vFN97Nnz9fVqvV9ZWXlxfpKYVETpe0oI4D4gH3AQBA/AsoPP/yl79UU1OTHn/8cZWUlGjGjBkaM2aMxowZo+nTp2vz5s367W9/q8bGRj3yyCNBmbDNZpMkHTx40O3xgwcPup6z2WytykUaGxtVVVXlNqatYzQ/h6cxzufb8sADD8hut7u+Pv/8c19fYkwYlp+lXGuaPFUzW3RqtW1YflY4pwVETJPD0MMrytRWgYbzsYdXlFHCAQAxLqDwvHHjRvXv31/33HOPxzGzZs3SgAEDtGHDhkBO5ZKfny+bzaY1a9a4HqupqdHmzZtVWFgoSSosLFR1dbW2bNniGrN27Vo5HA4NHz7cNWbdunVqaGhwjVm9erXOO+88de3a1TWm+XmcY5znaUtqaqoyMjLcvuJRYoJFc8cWSFKrAO38fu7YAm4WRIfBfQAA0DEEFJ4bGho0YMCAdsf179/fLaS25+jRo9q+fbu2b98u6dRNgtu3b9f+/ftlsVg0c+ZM/epXv9Lrr7+ujz/+WD/4wQ/Uo0cPV0eOfv36afTo0ZoyZYpKSkq0YcMGzZgxQ7feeqt69OghSfr+97+vlJQUTZ48WTt27NBLL72k3//+95o9e7ZrHnfffbeKior029/+Vrt27dK8efP0wQcfaMaMGeYvUhwb3T9XC28bLJvVvTTDZk3TwtsGU9+JDoX7AACgYwjohsG+ffuaKkv48ssvdd5555k+7gcffKDvfOc7ru+dgfb222/XkiVLdO+99+rYsWO68847VV1drUsuuURFRUVKS/smxP3973/XjBkzdOWVVyohIUE333yz/vCHP7iet1qt+te//qXp06dryJAh6tatmx566CG3XtDf+ta39Pzzz+sXv/iF/vu//1vnnHOOli1bpv79+5t+LfFudP9cXVVgY4dBdHjcBwAAHUNAfZ4XL16sqVOnau3atbr88svbHPPuu+/qiiuu0MKFC92CaUcRr32eAbhz9j6vtNe1WfdM73MAiF5h6/M8ZcoUzZw5U2PGjNHPfvYzffTRR6qtrVVtba0++ugjzZkzR2PGjNHMmTM7ZHAG0HFwHwAAdAwBrTwnJib6f2KLRY2NjX7/fKxg5RnoWOjzDACxx5e8FlDNcyA7e0f5ruAA4BfuAwCA+BZQeHY4HMGaBwDEjcQEiwr7ZEd6GgCAEAio5hkAAADoSAjPAAAAgEmEZwAAAMAkwjMAAABgEuEZAAAAMInwDAAAAJgUUKs6xKcmh0GP2iDjmgIAEB8Iz3DD7mjBxzX1jjcWAIBYEtD23GhfLG3PXVRaoWlLt6rlXwhnjFl422DCno+4pt7xxgIAEA18yWvUPEPSqdW/h1eUtQp5klyPPbyiTE0O3muZxTX1zvnGonlwlqRKe52mLd2qotKKCM0MAADPCM+QJJWUV7UKMc0ZkirsdSoprwrfpGIc19Qz3lgAAGIV4RmSpEO1nkOeP+PANfWGNxYAgFhFeIYkKadLWlDHgWvqDW8sAACxivAMSdKw/CzlWtPkqceBRadu5BqWnxXOacU0rqlnvLEAAMQqwjMkSYkJFs0dWyBJrcKe8/u5YwtoIeYDrqlnvLEAAMQqwjNcRvfP1cLbBstmdV/ts1nTOnxLNX9xTdvGGwsAQKyiz3OIxVKfZyc2rQg+rmnb6PMMAIgGvuQ1wnOIxWJ4BsKJNxYAgEjzJa+xPTeAiEpMsKiwT3akpwEAgCnUPAMAAAAmEZ4BAAAAkwjPAAAAgEmEZwAAAMAkbhhE3KKLg2dcGwAA/EN4Rlyif7BnXBsAAPxH2QbiTlFphaYt3eoWDiWp0l6naUu3qqi0IkIzizyuDQAAgSE8I640OQw9vKJMbe3843zs4RVlanJ0vL2BuDYAAASO8Iy4UlJe1WpVtTlDUoW9TiXlVeGbVJTg2gAAEDjCM+LKoVrP4dCfcfGEawMAQOAIz4grOV3SgjounnBtAAAIHOEZcWVYfpZyrWny1HTNolOdJYblZ4VzWlGBawMAQOAIz4griQkWzR1bIEmtQqLz+7ljCzpkT2OuDQAAgSM8I+6M7p+rhbcNls3qXn5gs6Zp4W2DO3QvY64NAACBsRiGQV+qEKqpqZHVapXdbldGRkakp9OhsIueZ1wbAAC+4UteY4dBxK3EBIsK+2RHehpRiWsDAIB/KNsAAAAATCI8AwAAACYRngEAAACTCM8AAACASYRnAAAAwCTCMwAAAGASreoA4D/ofw0AaA/hGQAkFZVW6OEVZaqw17key7Wmae7YAnZeBAC4ULYBoMMrKq3QtKVb3YKzJFXa6zRt6VYVlVZEaGYAgGhDeAZiSJPDUPHew1q+/UsV7z2sJocR6SnFvCaHoYdXlKmtK+l87OEVZVxrAIAkyjaAmEFZQWiUlFe1WnFuzpBUYa9TSXkVW5oDAFh5BmJBNJYVxMsq+KFaz8HZn3FtiZdrBQBg5RmIeu2VFVh0qqzgqgJb2DpDxNMqeE6XtKCOaymerhUAgJVnICwCWXn0pawgHKJxFTwQw/KzlGtNk6e3HRadCrvD8rN8Pna8XSsAACvPQMgFuvIYjrICs6JxFTxQiQkWzR1boGlLt8oiub025yuYO7bA59cTj9cKAMDKMxBSwVh5DHVZgS+ibRU8WEb3z9XC2wbLZnW/hjZrmhbeNtiv8op4vVYA0NGx8gyESLBWHp1lBZX2ujaPZdGpkOdPWYGvomkVPNhG98/VVQW2oO0wGM/XCgA6MlaegRAJ1sqjs6xAUqu63EDKCvwRTavgoZCYYFFhn2zdMOgMFfbJDuiaxvu1AoCOivAMhEgwVx5DUVbgj1DeXBdvuFYAEJ8o2wBCJNgrj8EuK/BHqG6ui0dcKwCIT6w8AyESipXHYJYV+CtaVsFjAdcKAOKPxTAMtroKoZqaGlmtVtntdmVkZER6OggzZ7cNqe2Vx1gOUE0OI6Kr4LGEawUA0c2XvEZ4DjHCM9hhDgCA6OZLXqPmGQixaKhVBgAAwUF4BsLAWasMAABiGzcMAgAAACYRngEAAACTCM8AAACASYRnAAAAwCRuGATCgD6/AADEB8IzEGL0eQYAIH5QtgGEkHOHwebBWZIq7XWatnSrikorIjQzAADgD8IzECJNDkMPryhTW1t4Oh97eEWZmhxs8gkAQKwgPAMhUlJe1WrFuTlDUoW9TiXlVeGbFAAACAjhGQiRQ7Weg7M/4wAAQOQRnoEQyemSFtRxAAAg8gjPQIgMy89SrjVNnhrSWXSq68aw/KxwTgsAAASA8AyESGKCRXPHFkhSqwDt/H7u2AL6PQMAEEMIz0AIje6fq4W3DZbN6l6aYbOmaeFtg+nzDABAjGGTFCDERvfP1VUFNnYYBAAgDhCegTBITLCosE92pKcBAAACRNkGAAAAYBLhGQAAADCJ8AwAAACYRHgGAAAATCI8AwAAACbFZHieN2+eLBaL21ffvn1dz9fV1Wn69OnKzs7WaaedpptvvlkHDx50O8b+/fs1ZswYpaenKycnR3PmzFFjY6PbmHfeeUeDBw9Wamqqzj77bC1ZsiQcLw8AJElNDkPFew9r+fYvVbz3sJocRqSnBAAdXsy2qjv//PP11ltvub5PSvrmpcyaNUurVq3SK6+8IqvVqhkzZuimm27Shg0bJElNTU0aM2aMbDabNm7cqIqKCv3gBz9QcnKyfvOb30iSysvLNWbMGE2dOlV///vftWbNGv3oRz9Sbm6uRo0aFd4XC6DDKSqt0MMrylRhr3M9lmtN09yxBWyuAwARZDEMI+aWMubNm6dly5Zp+/btrZ6z2+06/fTT9fzzz+u73/2uJGnXrl3q16+fiouLNWLECL355pu67rrrdODAAXXv3l2StGjRIt1333366quvlJKSovvuu0+rVq1SaWmp69i33nqrqqurVVRUZHquNTU1slqtstvtysjICOyFA+gQikorNG3pVrX8n7NzWx12pwSA4PIlr8Vk2YYk7dmzRz169NBZZ52lCRMmaP/+/ZKkLVu2qKGhQSNHjnSN7du3r3r27Kni4mJJUnFxsQYMGOAKzpI0atQo1dTUaMeOHa4xzY/hHOM8hicnT55UTU2N2xcAmNXkMPTwirJWwVmS67GHV5RRwgEAERKT4Xn48OFasmSJioqKtHDhQpWXl+vSSy9VbW2tKisrlZKSoszMTLef6d69uyorKyVJlZWVbsHZ+bzzOW9jampqdOLECY9zmz9/vqxWq+srLy8v0JcLoAMpKa9yK9VoyZBUYa9TSXlV+CYFAHCJyZrna665xvXfF1xwgYYPH65evXrp5ZdfVqdOnSI4M+mBBx7Q7NmzXd/X1NQQoAGYdqjWc3D2ZxwAILhicuW5pczMTJ177rn65JNPZLPZVF9fr+rqarcxBw8elM1mkyTZbLZW3Tec37c3JiMjw2tAT01NVUZGhtsXAJiV0yUtqOMAAMEVF+H56NGj2rt3r3JzczVkyBAlJydrzZo1rud3796t/fv3q7CwUJJUWFiojz/+WIcOHXKNWb16tTIyMlRQUOAa0/wYzjHOYwAwj5Zr5g3Lz1KuNc11c2BLFp3qujEsPyuc0wIA/EdMlm387Gc/09ixY9WrVy8dOHBAc+fOVWJiosaPHy+r1arJkydr9uzZysrKUkZGhu666y4VFhZqxIgRkqSrr75aBQUFmjhxoh577DFVVlbqF7/4haZPn67U1FRJ0tSpU/XUU0/p3nvv1Q9/+EOtXbtWL7/8slatWhXJlw7EHFqu+SYxwaK5Yws0belWWSS3GwedgXru2AIlJniK1wCAUIrJlecvvvhC48eP13nnnafvfe97ys7O1qZNm3T66adLkn73u9/puuuu080336zLLrtMNptNr776quvnExMTtXLlSiUmJqqwsFC33XabfvCDH+iXv/yla0x+fr5WrVql1atXa+DAgfrtb3+rP//5z/R4jiGsdkaes+VayxvgKu11mrZ0q4pKKyI0s+g2un+uFt42WDare2mGzZpGmzoAiLCY7PMcS+jzHBmsdkZek8PQJY+u9dg5wqJTYfC9+65gFdWDJoehkvIqHaqtU06XU6UaXCsACD5f8lpMlm0A3njaYMK52hnIyp2/YeZEfZN+80aZ9h0+rt7Z6frvawvUKSUxbOePBF9arhX2yQ7fxGJIYoKFawMAUYbwjLjS3gYTFp3aYOKqApvPodPf1ewp//u+Vpd9c3Pq+j3S3zbt11UFOVr8g4tCfv5IoeUaACAexWTNM+BJqDaYMFO721aNdcvg3NzqskOa8r/vB+380YaWawCAeMTKM+JKKFY7zaxmP/Dqx5r3+g5V1px0PZdzWrIOHW3weuzVZYd0or7JawlHKFfTQ8nZcq3SXtfm3J01z7RcAwDEElaeEVdCsdppZjX7yPEGt+Asqd3g7PSbN8oCPr+vq+n1jQ49u/5TPbS8VM+u/1T1jQ7TP2uWs+WapFY9i2m5BgCIVaw8I66EYrUz1DW5+w4fD8r5zY6b/0aZFq8vV/POfb9+Y6emXJqvB64tMHUMs5wt11rWatuiuFYbAABvCM+IK6HYYCLUNbm9s9ODcn4z4+a/UaY/rStv9bjDkOvxUAToqwpsMdMlBAAAbyjbQNwJ9gYT7W2XHKj/biesBmu75vpGhxavbx2cm1u8vjxkJRyFfbJ1w6AzVNgnm+AMAIhZrDwjLMLdn3h0/1xdfm5OUHore1vNDtRVBTntzilYq+l/K96n9jZZdBinxk2+9Kx25x4s9Y0O/a14nz6rOq5eWemaWNhbKUm8r5eiq693NM0FACKJ8IyQi0R/4pZ1vev3SH/fvN/vul5Ptbu51jTZTzToeH1Tmz9n0anw29hGar3gzAzTfZ6DUTv8WZX32mpfxwVDOOuvY0009fWOprkAQKSxPXeIdfTtuT3t9udcrwpktz9PPNX1Ov34Mv+DWcvVtyPHTuonz2/z61gW+f76A1n9e3b9p3pk1c52xz04pl9YVp5D+ecU6yLx7yYW5gIAoeJLXuOzUYRMe/2JpVP9iZvaqyXwQajrepvX7g7Lz2o3jLYXa319/YHUDk8s7K32hidYTo0LtUjWX0e7SPy7iYW5AEC0IDwjZEK12583vtT1Bqq91yd5r48Oxev3JiUpQVf2y/E65sp+OWGpNw7nn1OsicS/m1iYCwBEC8IzQiYUu/21J5x1vcGad6j7SDs1OQyVflnjdUzplzVhWUWMxvrraBGJfzeBniNcf4cBIBpwwyBCJhS7/bWnV5b3nsltjfO3jjhY8/b3OL7O28xKuXMVsbBPtl9zMsufP6eOIhL/bgI9RzjmAgDRgvCMkBmWn6XM9GRVH/e8TXXX9GSfdvtrz8TC3vr1Gzu9lgQ0r+sNpItAe7sZOs9lGG2Xbzh3OxzSq6uK9x72Kbz7M+9oWkX8/vBepm5e/P7wXiGfS7QJxS6Z8TAXAIgWlG0gooJdIJCSlKApl+Z7HTPl0nylJCW4ugi0XI2ttNdp2tKtKiqt8HocZ/9lqfWNgZb/fE25NN/jazQkXT8wV5c//rbGL96ku1/crvGLN+mSR9d6Pbe/846mVcTtn1cHdVw8ae/vleT7LpnxMBcAiBaEZ4RMSXmV11VnSao+3qCS8io1OQwV7z2s5du/VPHewwHV3T5wbYF+fFl+q84SCZZv2p8Fq4tAe7sZXtizq9ef/9O6cp9CcCDzDtZOhcEQTavg0SjYu2TGy1wAIBpQtoGQMRt83iqr1OyXtwd1A4YHri3QPVf39bhznS9dBNqr/x3dP1dXFdha1R9L0iWPrvV57oZOBdmHV5TpqgKb26qeP/NuXht960U99cRb/w5op8JANTkMfV170tTYjlxL6+nvVSRWeaNpLgAQaYRnhIzZ4PPshn2tHnOuvgayspWSlOBxs49gr3w6+y83V7z3cLs36HniKbybnU+l/YSK9x7WW2WVem37l6o69s0nAJnpyZLk9qmALzsVBqKtWm1Pgl0PH4va+nsVKdE0FwCIJMIzQsZZJuAtKCVY1ObNfd5WX4MhHPW/wSg5aHmMbp1TTf3cI6t2qupYfZvP2Y83yJA0a+Q56t2tc9hWET3tVOcJ224AAKIRNc8ImcQEi64f6H0l01tJcSg3YAhH/W8wSg6aH6OotEKzXjK3Fbin4Cx988bkxfc/13UX9PB5p0J/eKvV9sRZDw8AQDQhPCNkmhyGXv/Qe8cKM0Jx01g4uggM6dW13e2wPWkZ3otKKzR16VYdOuo5FPsi3DvDmekx3ZaOesMgACB6EZ4RMv4GppZCddNYqLsIbPnsSLtbULelZXhvchia/fKH7f5c1//UMvsiXOHU3/N05BsGAaCjCmYHrlCg5hkhE2gwC8cGDKHsImD29Wd2Slb1Cc83723c87WO1ze1e5xbhuZp0bpPfZpjuMKpP+cJV9s8AED0CGTzsnAhPCNkfAlMkWydFqouAmZf/9MTBivBYvEY3v+x7QtTx/n4S7vpuYV7ZzgzuzG2dP3AXFqhAUAH4unG8mB04AomyjYQMmZvyvvj9y8MSelEpD/2Mfv6R5yVrcI+2bph0Blt3rxnZtVZktJTEr2er6Vw7gznrcbck9c/rIi6j+oAAKERrM3LwoGVZ4SMMzBNW7rV68ry6P65GtU/16/SieYbgDT/uaLSCs17vUyVNd987GPLSNO860PzsY+neZh5/S1fZ8tjDe3VVf8qO9juHIblZ+vmIWe2eb7mIvXxl7PG3Gyf5wp7nZZsKFe3LqkxtymHp78PkToOAES7YG5eFmoWwzAiH+HjWE1NjaxWq+x2uzIyMiI9nYgIVf2Sp+NePzBXf1pX7vHnFgX5Y5/2Xp8vr7+tsd27pOhQbb3XcgeLRdr9yDVKSUpo8xjZnVN0w6AeuqrAFvEA1uQw9LvVu/XU23t9+rloq3nzJFh/32Oh7g8AgmX59i9194vb2x33+1sH6YZBZwT9/L7kNcJziEVreA73ylgwztf8GPu+PqbfvbXH5/lKp7pSfPCLq4ISID3VZzmP7Cw9MfP6vR2rvX+k112Qq9/feqHrmNG+Ylm897DGL97k08+0vKbRyOzfh3AdBwBihdnfCy9MGRGSlWdf8hplGx1QJFbGAr0pz5dtndtz5HiDNn16WBef3S2g47RXn9Vyh0Rvr9/MsVKSEnSy0dHmz6/8qEJbPjviuvbRvpWyPzcQhnrXyUD5+vch1McBgFjS3u+FcN/o7g03DHYwzhWtliHUeSdrUam5TU18PU4gN+95OlcgivceDvgYvtRn1Tc69Oz6T/XQ8lI9u/5T1bcIwWaO5Sk4O/n6ZxhJzW8g9EW4N3fxhS9/H8JxHACIJeHYvCxYWHnuQCK1MhbISrc/2zqbE/gRzfZxXrx+ryb8+Su3DVN+/cZOTbk0Xw9cW+DTsbwJ96pkoKUho/vn6s7L8rV4fbnPm8lE486DZufU3rhgHQcAYo2nG8tb7n8QaYTnDiRYd7L6chz7ifo2azcrTPZsDNYuhS0VntV2yUZ7gbD581/XnjR1rrW7vmr1mMOQ66bGB64tCNpmJS3/DENV+xyM0p+i0go9s67cr7cx0bjzoNk5tTcuWMcBgFgUys3LgoXw3EGcqG/S02+bu8EuWCtjlfYTeuyfuz2GI0PSA69+7HWVNBSra5npyRrRxpsDf7pmJFjk1xbcTovXl+ueq/v6VQPszaHaupB2OQm0ib2/nyhEU81bS8Gq14uluj8ACIVov2+HmucOYMr/vq9+DxXpvU/M1fkGa2Ws6lh9u6vGR4436Km1nkO9r6trZt6XLrhpgMdOF55quOe/Udbm856Cs9n3xw5D+lvxPr82EfGm/KtjmhqE2vaWgtXE3p9PFKKt5q2lYNXrxVLdHwB0RITnODflf9/X6rJDpsY6d7xrbHTo//1zl/7fP3drwydftwpCzpUxb3Ktaco6LdXUeZ/bsM9j2Gpvl76WbNY0LbptsBbdNli2DPfz2zJS2+zx3F4gNHRqhdhbHGyZY2zWNH37XHPdPD6rOi7pm1qv7hn+fxxvkZTZKUl/8PCGJNBdmoJ1M5s/nygEY9fJUHP+GQa6Y2awjgMACD7KNuLYifomn4KzIcl+okETnytxPf7U258oMz1ZC24a4PqFnZhgaXcjkqG9MpXTxVx4rj7R4LHO2swufTNHnqve3dJb1UWZrZkyswraXs50GNKDY/q57Yb3l/fK9c6/v/b+g5Lyuqa7/nt0/1x1SU3WhGc3t/tzbTEkVZ9obHeMv7s0BetmNrOfKLS8prGw2hqser1YqPsDgI6I8BzHfvNGmemxmenJOnK8Qcfrm1o9V328QVOXbnWt2jY5DL3+ofeP/Vd8VKmS8iNKT0ls85gteQtb/t59a7ZmKlh11d26pLrtetS3exdTP9dy3NfHzN2I2FJmp2TJcurPywx/XnewbmYzW9d7x8X5MRkWg1WvF+11fwhMtG9mBKBthOc4tu/wcdNjmxzeewhL39zcZ7Ze9aDJbhRS+2ErlKtwwepa0PI4Xx+vN/VzLcf5O5/p3zlbv35jp+nxX9ee1PLtX/q0M+SQXl2DcjObmU8UqOtFPGP7dSB2EZ7jWO/sdK03uYN1TV37q8POnfm+Pmo+FDujj6eqB186B4RqFc5MpwtvW2R7eg1mW9m1HDekV1dldU5W1TFzK8jO83c7LcXUeOlUjfYjq74J2m390vb0y/36gbl6Zl15wKHX+YnCvNfLVFkTvn6erPYh0oLRsQZA5HDDYBz772t938GtPcV7D/u0Muq84c6bSK8wmul04S04S22/hiMmV56bjysqrdDlj7/tU3B2nt9m7WTqZ6TWNdwtu3B46z7yzLpy3XlZfhBvZnOfjGEEf0scp6LSCl3y6FqNX7xJd7+4XeMXb9Ilj66NiV0ZER+C1bEGQOSw8hzHOqUk6qqCHNM3DZpjBLUn8ciCnIitsLRcgXz6+xfqkVU7fWqh5m2V1Oz7Aec4T6tRLcc2/53a/PxNDsPvP5fmuxNe0bd7uztIvv5hhd6d8x1t+eyI3yu4nl7vwZqTAa++tbW6vLqsktU+RFywNqsCEDmE5zi3+AcX6eaFG7Tls+qgHK/wrG5u9aqBWrPzkOobHUpJCu+HIJ5KEh4cUyBrp2RNf36rqk94Xv3NTE/W0+MHa0SfbI+BcXjvbD2lve3OZXjvbFObhmR3TtF7912h7Z9XtxlYvdURm+H8pf234n2mfrm/v69KCRb/PjEwu/rmzzbjbf3Z2jJSVdfoCHhreiBQbL8OxD7Ccwcw+eJ8bdu/LaCd8CQpPSVR0qng46xXfWjZxzp01FyJQVschvTXjeXqf0Zm0GtQPdW2eqs3nP78Vs0ceY7X4Cyd6miRkGDxuHV3Tpc007udJCRaTN2EefhYvbZ/Xu11NcpTZ5Jca5qu7W/Tsxv2tTsfZ9/p9kz/u/sbDF9udjLzev1ZffP4Z1vjvf6c1T6EC9uvA7GP8BznikorNP35bUHZ8vl4fZMmPLvZLSR9XnXCpw4PbfndW3vc2tkFawvpljeiZXVO0Q0Dc7X8wwqvK5DPmQiYkvvKUFurndY0c/+8DtXUqfRLu8/n9MRTZ5KS8ipT4blXVnq7YyS1eoPhS/lD8z+XYIyT/N/yuzlW+xBqbL8OxD7Cc5xpchja9OlhFe89rPrGJj33nved8fxRYa/T1KVb9dStF+r9fd53kjOjZR/oQGtQi0orNLWNkpKqY/V6buNnXn/21CYj5lbSV3z4pTLTkvXunq/0lzZCqb3O+2YlThs++Vr/t/VLU2O7nZaq4r2H212lb6szidlf2hMLe+vP75X7XDvdsm7aWz10lcmOLWbHSf5t+d0Sq30INdo0ArGP8BxHikordP+rH5veJCNQM17cFpLjBlLz2uQwdP+rH4dkXi29tfMrvbXzq4CPs3rnQVPjOqck6p6Xt7uVIPiySu/8pd3WGwvp1HWfO7ZAKUkJftdOO8sfRsxfo6pj33QRaTnPrM7m2uqZHScFtmrMah/Cyd+NnwBEB8JznPC02hrL/KlB3fTp4bC9eQgWezvbaTsdq2/SsRar9BUh6hTh6Zd7ZnqyqevbPDhLrT9NMNtWz5f2e/6uGjdf7WtyGFqyoVyfVR1Xr6x0TSzsHfabWdExsP06ELsIz3GgyWFo3uvmt+KOJb7UvErSe58EvhIcTv50xWjJkLlVemdNsLe5ND9OW7/cG5scmviXEr/m2Pz4Q3p1bdV2r6UEy6kNY8wyU5ZiTU9WWlJim5uybNt/RD/5+1a3Of36jZ2acmm+HghBz3Qz2NAlvrH9OhCbCM9xoKS8yueQGSt8qXmVpI8+N3fjXbQIVj26mVV6f/rLtvzlvmHP137PsfnxJe/B2fn8ls+OmA4XZmpJF9w0oM3VvseKdupP68rbnIPz8XAHaLZvBoDoxOeRcSCeOwT4UvMqfdNOryOqtJ/w+nww+st+fcy3NzOejh+qXrfOchNvux863xDcMOgMFfY51WN78frWwbm5xevLVd/o8GkugfC2w2PznSABAOHHynMciOcOAVXH6tXkMNr8qLqtj7SH5Wdr9U7fdlQcN7CHln14IFhTjphDtd8E2/pGh/5WvE+fVR1XXtd09bV10Z6DtaaO462jRzD+rvlyDH/O52st6d+K95laBf9b8T5NvvQsn+fjq/Y2kGFDFwCILMJzHBiWnyVbRlpclm48smqn/vxeeauPqj19pP3za/rJYpEMH+ohLu+bo837qoKy3XhzmZ2STbe9C4YFb+5S1X9WhhevL/d5UxyLTt0Q6K2jRyBbs7fsaBHKXrdma0mbHIY2l5trt2h285hAsX0zAEQ3yjbiQGKCRfOuj8wNTeHg7Cv9+7f+rSaH4fEj7Qp7nWa8uE09u5rv0CCdqqt+cEy/oM332+d204Nj+umaAd2DdkwzDJ2qz/3TOv+CsyHpyPGGVrvxNS8VcNYVO3+m5THa+u/m3zv715o5Tqh73RaVVuiSR9fqX2XmWgWa3TwmUGzfDADRzWIYvqzRwVc1NTWyWq2y2+3KyMgI6bnC3ee5JVtGqmrqGlttehLscxw92aSjJ821dzMr15qm/mdkaHWZbyUfzbXsHpGekhjSaxFMudY0nWho8vh3x7kS/N59V7i2OPd0M5sk0ze6ReqmOE/beHuz85ej1SkMNfXFew9r/OJN7Y57YcoIVp4BIEh8yWuE5xALZ3iW3HcYPNnQqPf3VWl3Za1ONIbmj/nBMf3UrUuqcrqkyWEYmvDnzSE5T0cWjHZ2nlxd0F3D87N0bk4XTXyu/RZ0zQObtzZqvrRYC3c7tiaHoUseXevzboThCqvO+bVX0uJ8IwMACJwveY2a5ziTmGDRxWd3U21dQ6sVvVDo1iVVNww6Q5K0fLu5LabhrlOSxe3NTcsVbJufNcZm/KvsoP5VdlDWTsmmxjcvFfBWV+xL/9pw97r1dxvvcJVJsH0zAEQ3wnMc8ucjaX9lpafo2fWf6rOq4+JDDP/Mvrqv+p9hda28DunVVVs+O+L6vvTLav36jV0hnYPd5I2N8dDZxd8QHM7XzvbNABC9CM9xxlubq1DwZ7c5fCPBIt3+rdZbQDdfiX0zCnr6Btr9Ipr4GoIj9drZvhkAohPhOc74+5E0ImPKpfmtgnNL4ery4Em8lQr40m4v0q+d7ZsBIPrQqi7O0L4qdlxVkGNqy+eJhb1lNrelthPEzeic6t5RovnufPHAW5u8luLttQMAAsfKc5yJh5rUSOuckqhjIW4xZ5H0VtkhFZVWtBvM1u46qLRkc23vTgZhC+krzjtdg/K66rOq4+qVla6Jha3LSqJdex08PNYUZ6Rq/LCe6t2tc0jKJMLdWQQAEHy0qguxSLSq86cNF05JsEjP/mCoJv31g5Cfy0zLsXDe/OmUmmTRyWbdP8LRdzmYfOkdHc4wG6me1gCA9tHnOYqEOzxLkQlc8aRrepKOHA/uJizeeOofHC1vhJxRMhbKFzz93Y/0a4jWeQEATvElr8XWZ7EwxfmRdFbnlEhPJSaFMzhLnuvUo+XmT2fge3hFmZp83ff7P5ochor3Htby7V+qeO9hv4/T3jk8dZoJxmvwV7TOCwDgH2qe49jRushs0w3feKpTj6abPw1JFfY6lZRX+dz9IVzlCu292QjkNcTjvAAA/mHlOQ4VlVZo6tKtqm9iJSvaZaYne+wfHI03f/oa6J3lCi3DY6W9TtOWblVREHtYm51buN+UROu8AAD+ITzHmSaHoXmvl0V6GjDJ261pzn7E0cSXQG+2XKG+0RGUkg6zcwv3m5JonRcAwD+UbcSZkvIqVdawgtWetKQE1QWhrVugjhxv8PhxvbMf8dSlW00dy5aRqnnXn69t+49o8fpyBbuENtfHXfbMliuMmL9GVcfq3c7jT0lHe5ufRGqnwGidFwDAP6w8xxk++jUnMTF6eut6+zMb3T9Xi24brMz0ZK/HmDXyXG24/0pJ0jPrfA/OtoxUXVWQ43XM9QNzfWrjZvbvYvPgLPlf0uFt85NI7hQYrfMCAPiH8Bxn+Oi3fRZJx06GdhMUXxyqOemxVKHJYcjaKUVzx56vwvzsVuErwSL9+LJ83T3yHEnyWCbRHsOQ3t93xOuY1z+s8Kmkwt+/i8Z/vvzpQOHsNGNrUe4S6Z0Co3VeAADf0ec5xCKxScrFC9ZSuhFE6SnmdvcLRFulCm11qWiLRaf6BFs7pWj84k0hnaenntRtcfap9lSuEOzztTx3NO7kF63zAoCOjj7PHVhigkWDe2ZGehpx4dr+3fXClBGaNfLckJ+rZamCpy4Vnjy8okwV1SdCOUVJvpUFeStXMMvfN4GJCRYV9snWDYPOUGGf7KgJqNE6LwCAeYTnOPPrVTv0RmllpKcR9VLbqXnump6sJ78/RMPys9TX1sXv8GdWy+4TvpRfOG+82/a597KLYPC1FMNTuUKXtERTP/917UmfzgcAQKjRbSOOzH+jTIvX74v0NGLCyXZ6YH9v6JlaXVZpqmwiWJwh+G/F+/w6Z1MIK7AC6Qgxun+uriqwuZUrrPv3IS1899N2f/bI8fp2xwAAEE6E5zhR3+jQ4vXlkZ5G3PjTunJJkbmen1Ud9+vnlm/7MsgzOSUYHSGc5QpOGz75ytTPUdUAAIg2lG3Eib8V7wt6X194lp6SqAeu6au/TrpImZ28t5HzVa+sdL9+7lh9YH2rLTq146EtI/QdIQrP6hbUcQAAhAsrz3FiS3nwtjlG+47XN+mCMzNV2CdbC24eoGlLt/rdUcLJWRoxsbC3/vxeeUBdKsycy2jxvSQtuGlAqxKLUHSEGNEnW5npyao+3uBxTGZ6skb40WkDAIBQYuU5TrxRVh3pKXQ4zs4Tzpvisjqn+H2s5qURKUkJAXep8GbWyHO89hsOR0eIxASLbhl6ptcxtww9k24UAICow8oz4KfmnSdG98/VFX27a8T8t1R1zPNqqie2Fn2enYE8mDcsOle2Z1xxjmZccU5E+w03OQy9/qH3T0te/7BC947uR4AGAEQVwjPgh67pya06T6QkJeg3N54q4ZBkuuTiwTH9dMfF+a1CorNLxZIN5Xpk1c6A5tvWTX/+bD4SLCXlVe2+Kaiw16mkvCqi8wQAoCXKNuJAk8NQcjt9ixFcnoKxp77GbbHo1M6CbQVnp8QEi+64OF+5Jo7nPGa4bvoLhNnNVnzZlAUAgHBg5TkOzHxxqxra6VsM6bTUJB092djuuC5pSaqt8z6u+niDx1XR5n2NV5dV6i8b9nm8Qc9M+zfnTn3t3ZQY7pv+AmF2sxVfN2UBACDUWHmOcfWNDq38iB0FvZl8cW+9MGWEtj54ldJTvP+VT0tK0LzrCkwd19uqqPOmu4fGnq9FbaxE+7oS7FzR9rYCHe6b/gIxLD9LudY0jzdEOlfl/dmUBQCAUGLl2aSnn35ajz/+uCorKzVw4EA9+eSTGjZsWKSnpb8V7wtZO7NYZ8tI1bzrz3cLqP/zvUGa+p+a5LY8cesgWTuZ65phdlW0rR32/FkJbnmcbqelSob09bGTUbm67E3z1fRAVuUBAAg3wrMJL730kmbPnq1FixZp+PDheuKJJzRq1Cjt3r1bOTk5EZ2bv7vRxbtZI8/VjCvObvMmvEW3Ddbc5Tt0sPak6/HmQbvJYSjXmuaxz7I/W1W33GHPX8E6TjTw1FGkZecRAACiicUwDBYu2zF8+HBddNFFeuqppyRJDodDeXl5uuuuu3T//fd7/dmamhpZrVbZ7XZlZGQEfW7Prv804E4Msera/jZt3V+typpvgleuyeDV5DC8rgQXlVa02TXDOSKabr6Lde39WQAAEGq+5DXCczvq6+uVnp6u//u//9O4ceNcj99+++2qrq7W8uXL3cafPHlSJ09+s6JZU1OjvLy8kIXn+kaHzvvFm3FbujG0V6Y+OXRM1Se+6Z3cPCCHMngVlVa0WhU1G84BAEDs8CU8U7bRjq+//lpNTU3q3r272+Pdu3fXrl27Wo2fP3++Hn744XBNTylJCfrRpb21eP2+sJ0zVNKSE9Srayf1zO6sYfnZuv1bvZWSlOA1IIeyjCFYtcoAACB+EJ6D7IEHHtDs2bNd3ztXnkPp52PO166KWq3/5HBIz/Pt807Xxn9/pXqTy9ynpSRowU0X6JoLemjTp4dVvPewJEOFZ3XTiP8EXjPBNJJ1vvFUYwwAAAJHeG5Ht27dlJiYqIMHD7o9fvDgQdlstlbjU1NTlZqaGq7pufztRyN01v2r5AjBsVveTOcMvJmpyVq966DKDx9TenKirirortq6RmV1TpHN2sktDF98djddfHa3VscmmAIAgFhCeG5HSkqKhgwZojVr1rhqnh0Oh9asWaMZM2ZEdnItfLpgjM7571Vq8CNBJ0i668qzNSw/W4dqT6rq6Mk2Q3DLldjL+0W22wgAAEA4EZ5NmD17tm6//XYNHTpUw4YN0xNPPKFjx45p0qRJkZ5aK3t+M0ZfVp3QNX94V0frmpScaNG1F3RX55RkZaSlKCFBGnRmV71Qsk//PnRM1k7Jmn3Vebr8vNOp5QUAAGgH4dmEW265RV999ZUeeughVVZWatCgQSoqKmp1E2G0OCOrkz6aN9rrmJHnR+fcAQAAohmt6kIs1H2eAQAAEBhf8lpCmOYEAAAAxDzCMwAAAGAS4RkAAAAwifAMAAAAmER4BgAAAEwiPAMAAAAmEZ4BAAAAkwjPAAAAgEmEZwAAAMAkwjMAAABgEuEZAAAAMInwDAAAAJiUFOkJxDvDMCRJNTU1EZ4JAAAA2uLMac7c5g3hOcRqa2slSXl5eRGeCQAAALypra2V1Wr1OsZimInY8JvD4dCBAwfUpUsXWSyWkJyjpqZGeXl5+vzzz5WRkRGSc8Qqro1nXJu2cV0849p4xrXxjGvjGdfGs3BfG8MwVFtbqx49eighwXtVMyvPIZaQkKAzzzwzLOfKyMjgH58HXBvPuDZt47p4xrXxjGvjGdfGM66NZ+G8Nu2tODtxwyAAAABgEuEZAAAAMInwHAdSU1M1d+5cpaamRnoqUYdr4xnXpm1cF8+4Np5xbTzj2njGtfEsmq8NNwwCAAAAJrHyDAAAAJhEeAYAAABMIjwDAAAAJhGeAQAAAJMIzzHu6aefVu/evZWWlqbhw4erpKQk0lMKufnz5+uiiy5Sly5dlJOTo3Hjxmn37t1uY+rq6jR9+nRlZ2frtNNO080336yDBw+6jdm/f7/GjBmj9PR05eTkaM6cOWpsbAznSwmpBQsWyGKxaObMma7HOvJ1+fLLL3XbbbcpOztbnTp10oABA/TBBx+4njcMQw899JByc3PVqVMnjRw5Unv27HE7RlVVlSZMmKCMjAxlZmZq8uTJOnr0aLhfSlA1NTXpwQcfVH5+vjp16qQ+ffrokUceUfN7yTvKtVm3bp3Gjh2rHj16yGKxaNmyZW7PB+s6fPTRR7r00kuVlpamvLw8PfbYY6F+aQHzdm0aGhp03333acCAAercubN69OihH/zgBzpw4IDbMTritWlp6tSpslgseuKJJ9we78jXZufOnbr++utltVrVuXNnXXTRRdq/f7/r+aj8vWUgZr344otGSkqK8Ze//MXYsWOHMWXKFCMzM9M4ePBgpKcWUqNGjTKee+45o7S01Ni+fbtx7bXXGj179jSOHj3qGjN16lQjLy/PWLNmjfHBBx8YI0aMML71rW+5nm9sbDT69+9vjBw50ti2bZvxxhtvGN26dTMeeOCBSLykoCspKTF69+5tXHDBBcbdd9/teryjXpeqqiqjV69exh133GFs3rzZ+PTTT41//vOfxieffOIas2DBAsNqtRrLli0zPvzwQ+P666838vPzjRMnTrjGjB492hg4cKCxadMmY/369cbZZ59tjB8/PhIvKWh+/etfG9nZ2cbKlSuN8vJy45VXXjFOO+004/e//71rTEe5Nm+88Ybx85//3Hj11VcNScZrr73m9nwwroPdbje6d+9uTJgwwSgtLTVeeOEFo1OnTsaf/vSncL1Mv3i7NtXV1cbIkSONl156ydi1a5dRXFxsDBs2zBgyZIjbMTritWnu1VdfNQYOHGj06NHD+N3vfuf2XEe9Np988omRlZVlzJkzx9i6davxySefGMuXL3fLMdH4e4vwHMOGDRtmTJ8+3fV9U1OT0aNHD2P+/PkRnFX4HTp0yJBkvPvuu4ZhnPofeXJysvHKK6+4xuzcudOQZBQXFxuGceofdEJCglFZWekas3DhQiMjI8M4efJkeF9AkNXW1hrnnHOOsXr1auPyyy93heeOfF3uu+8+45JLLvH4vMPhMGw2m/H444+7HquurjZSU1ONF154wTAMwygrKzMkGe+//75rzJtvvmlYLBbjyy+/DN3kQ2zMmDHGD3/4Q7fHbrrpJmPChAmGYXTca9PyF32wrsMf//hHo2vXrm7/nu677z7jvPPOC/ErCh5vAdGppKTEkGR89tlnhmFwbb744gvjjDPOMEpLS41evXq5heeOfG1uueUW47bbbvP4M9H6e4uyjRhVX1+vLVu2aOTIka7HEhISNHLkSBUXF0dwZuFnt9slSVlZWZKkLVu2qKGhwe3a9O3bVz179nRdm+LiYg0YMEDdu3d3jRk1apRqamq0Y8eOMM4++KZPn64xY8a4vX6pY1+X119/XUOHDtV//dd/KScnRxdeeKEWL17ser68vFyVlZVu18ZqtWr48OFu1yYzM1NDhw51jRk5cqQSEhK0efPm8L2YIPvWt76lNWvW6N///rck6cMPP9R7772na665RlLHvjbNBes6FBcX67LLLlNKSoprzKhRo7R7924dOXIkTK8m9Ox2uywWizIzMyV17GvjcDg0ceJEzZkzR+eff36r5zvqtXE4HFq1apXOPfdcjRo1Sjk5ORo+fLhbaUe0/t4iPMeor7/+Wk1NTW5/WSSpe/fuqqysjNCsws/hcGjmzJm6+OKL1b9/f0lSZWWlUlJSXP/Tdmp+bSorK9u8ds7nYtWLL76orVu3av78+a2e68jX5dNPP9XChQt1zjnn6J///KemTZumn/70p/rrX/8q6ZvX5u3fU2VlpXJyctyeT0pKUlZWVkxfm/vvv1+33nqr+vbtq+TkZF144YWaOXOmJkyYIKljX5vmgnUd4vXfWHN1dXW67777NH78eGVkZEjq2Nfm0UcfVVJSkn7605+2+XxHvTaHDh3S0aNHtWDBAo0ePVr/+te/dOONN+qmm27Su+++Kyl6f28lheSoQJhMnz5dpaWleu+99yI9lYj7/PPPdffdd2v16tVKS0uL9HSiisPh0NChQ/Wb3/xGknThhReqtLRUixYt0u233x7h2UXWyy+/rL///e96/vnndf7552v79u2aOXOmevTo0eGvDXzX0NCg733vezIMQwsXLoz0dCJuy5Yt+v3vf6+tW7fKYrFEejpRxeFwSJJuuOEGzZo1S5I0aNAgbdy4UYsWLdLll18eyel5xcpzjOrWrZsSExNb3XF68OBB2Wy2CM0qvGbMmKGVK1fq7bff1plnnul63Gazqb6+XtXV1W7jm18bm83W5rVzPheLtmzZokOHDmnw4MFKSkpSUlKS3n33Xf3hD39QUlKSunfv3iGviyTl5uaqoKDA7bF+/fq57uh2vjZv/55sNpsOHTrk9nxjY6Oqqqpi+trMmTPHtfo8YMAATZw4UbNmzXJ9etGRr01zwboO8fpvTPomOH/22WdavXq1a9VZ6rjXZv369Tp06JB69uzp+v/yZ599pnvuuUe9e/eW1HGvTbdu3ZSUlNTu/5uj8fcW4TlGpaSkaMiQIVqzZo3rMYfDoTVr1qiwsDCCMws9wzA0Y8YMvfbaa1q7dq3y8/Pdnh8yZIiSk5Pdrs3u3bu1f/9+17UpLCzUxx9/7PY/LOf/7Fv+Q44VV155pT7++GNt377d9TV06FBNmDDB9d8d8bpI0sUXX9yqneG///1v9erVS5KUn58vm83mdm1qamq0efNmt2tTXV2tLVu2uMasXbtWDodDw4cPD8OrCI3jx48rIcH9V0FiYqJrVagjX5vmgnUdCgsLtW7dOjU0NLjGrF69Wuedd566du0aplcTfM7gvGfPHr311lvKzs52e76jXpuJEyfqo48+cvv/co8ePTRnzhz985//lNRxr01KSoouuugir/9vjtrf5yG5DRFh8eKLLxqpqanGkiVLjLKyMuPOO+80MjMz3e44jUfTpk0zrFar8c477xgVFRWur+PHj7vGTJ061ejZs6exdu1a44MPPjAKCwuNwsJC1/PO1jZXX321sX37dqOoqMg4/fTTY74lW0vNu20YRse9LiUlJUZSUpLx61//2tizZ4/x97//3UhPTzeWLl3qGrNgwQIjMzPTWL58ufHRRx8ZN9xwQ5ttyC688EJj8+bNxnvvvWecc845MdeOraXbb7/dOOOMM1yt6l599VWjW7duxr333usa01GuTW1trbFt2zZj27ZthiTjf/7nf4xt27a5OkYE4zpUV1cb3bt3NyZOnGiUlpYaL774opGenh71Lce8XZv6+nrj+uuvN84880xj+/btbv9fbt7toCNem7a07LZhGB332rz66qtGcnKy8cwzzxh79uwxnnzySSMxMdFYv3696xjR+HuL8BzjnnzySaNnz55GSkqKMWzYMGPTpk2RnlLISWrz67nnnnONOXHihPGTn/zE6Nq1q5Genm7ceOONRkVFhdtx9u3bZ1xzzTVGp06djG7duhn33HOP0dDQEOZXE1otw3NHvi4rVqww+vfvb6Smphp9+/Y1nnnmGbfnHQ6H8eCDDxrdu3c3UlNTjSuvvNLYvXu325jDhw8b48ePN0477TQjIyPDmDRpklFbWxvOlxF0NTU1xt1332307NnTSEtLM8466yzj5z//uVvo6SjX5u23327z/y233367YRjBuw4ffvihcckllxipqanGGWecYSxYsCBcL9Fv3q5NeXm5x/8vv/32265jdMRr05a2wnNHvjbPPvuscfbZZxtpaWnGwIEDjWXLlrkdIxp/b1kMo9k2UgAAAAA8ouYZAAAAMInwDAAAAJhEeAYAAABMIjwDAAAAJhGeAQAAAJMIzwAAAIBJhGcAAADAJMIzAAAAYBLhGQAAADCJ8AwAAACYRHgGAAAATCI8A0AcKykp0b333qthw4bJZrMpJSVF3bt319ixY/XWW2+1Gr9kyRJZLBbdcccdqqqq0syZM9WnTx+lpqbq29/+ttvYNWvW6KabblJubq5SUlKUk5OjG2+8UcXFxUGZCwBEI4thGEakJwEACI2RI0fq7bff1vnnn6+8vDx17txZe/fu1datWyVJTzzxhO6++27X+CVLlmjSpEkaM2aMysrKVF1drUsvvVSpqalKSUnR0qVLJUk/+9nP9Nvf/lYJCQkaOnSoevXqpf3796ukpEQJCQlavHixJk2aFNBcACAaEZ4BII69+eabGjRokHJzc90eLy4u1ujRo3XixAmVl5frjDPOkPRNeJakK6+8Uq+++qoyMjLcfnbx4sW68847dfbZZ+sf//iHLrjgAtdz69at03XXXaeTJ0+qtLRU55xzjt9zAYBoRNkGAMSxa665plVYlaTCwkJNnz5dDQ0NWr58eavnk5OT9cwzz7QKzg6HQ/PmzZMkvfjii27BWZIuu+wyPfjgg6qvr9ef/vSnoMwFAKJJUqQnAAAIrcOHD2vVqlUqLS3VkSNH1NDQIEnas2ePJGn37t2tfubCCy/UWWed1erxbdu26cCBA+rTp4+GDBnS5vmctdEbN24MylwAIJoQngEgji1evFizZs3SsWPHPI6pqalp9Vjv3r3bHPvpp59Kkvbu3SuLxeL13F999VVQ5gIA0YTwDABxasuWLfrxj3+sxMREPfrooxo7dqx69uyp9PR0WSwWPfPMM/rxj3+stm596dSpU5vHdDgckiSbzaZRo0Z5PX+3bt2CMhcAiCaEZwCIU6+88ooMw9Bdd92le++9t9XzzlIJX+Tl5UmSsrOztWTJkojOBQAigRsGASBOVVVVSZJ69erV6rm6ujr94x//8PmYF110kbp166aysjLt2LEjonMBgEggPANAnOrXr58k6a9//atqa2tdj9fV1eknP/mJysvLfT5mcnKy5s6dK8MwdOONN+q9995rNaapqUlr167Vpk2bQjoXAIgEwjMAxKlJkyapV69e2rZtm/Lz83XjjTfqu9/9rnr16qX/+7//83tDkhkzZmjOnDnas2ePLr30UvXv31/jxo3T+PHj9Z3vfEfdunXTlVdeqe3bt4d8LgAQboRnAIhTmZmZ+uCDD/STn/xEmZmZevPNN1VcXKyrr75aW7du1aBBg/w+9mOPPaYNGzZowoQJOnr0qIqKirRq1SodOHBA3/72t/XnP/9Zt9xyS1jmAgDhxA6DAAAAgEmsPAMAAAAmEZ4BAAAAkwjPAAAAgEmEZwAAAMAkwjMAAABgEuEZAAAAMInwDAAAAJhEeAYAAABMIjwDAAAAJhGeAQAAAJMIzwAAAIBJhGcAAADAJMIzAAAAYNL/B7wCab9KgGz0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 初始化绘制画布的大小\n",
    "plt.figure(figsize=(8,6))\n",
    "# 绘制散点图\n",
    "df['room_price'] = pd.to_numeric(df['room_price'], errors='coerce')  # 将字符串变成浮点数\n",
    "plt.scatter(x=df['area'], y=df['room_price'])\n",
    "# 设置X轴刻度\n",
    "# plt.xticks(ticks=[0, 50, 100, 150, 200], labels=['0', '50', '100', '150', '200'])\n",
    "# plt.yticks(ticks=[0, 1000, 2000, 3000, 4000, 5000], labels=['0', '1000', '2000', '3000', '4000', '5000'])\n",
    "plt.xticks()\n",
    "plt.yticks()\n",
    "plt.xlabel('area', fontsize=16)\n",
    "plt.ylabel('price', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1218f1a2-db90-4bc5-bde8-42f322397100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>room_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.0</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.0</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>35.0</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>40.0</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>28.0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>600.0</td>\n",
       "      <td>12500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>25.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1354 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       area  room_price\n",
       "0      32.0         199\n",
       "1      28.0         333\n",
       "2      35.0         481\n",
       "3      56.0         527\n",
       "4      35.0         338\n",
       "...     ...         ...\n",
       "1349   35.0         580\n",
       "1350   40.0         550\n",
       "1351   28.0         180\n",
       "1352  600.0       12500\n",
       "1353   25.0          99\n",
       "\n",
       "[1354 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['area', 'room_price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39c4e7a0-af76-4b65-900a-9678782c1bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 去除异常值\n",
    "# 也就是哪些面积很小，又不是复式别墅，还特别贵的情况\n",
    "# 或者面积很大，但非常便宜的情况\n",
    "df = df.drop(df[(df['area'] > 600) & (df['room_price'] < 4000)].index)\n",
    "plt.figure(figsize = (8, 6))\n",
    "plt.scatter(x = df['area'], y = df['room_price'])\n",
    "plt.xlabel('area', fontsize = 16)\n",
    "plt.ylabel('price', fontsize = 16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b0020a1-2e26-46a1-8951-87b49b087aca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "偏度6.166331339048902\n",
      "峰度55.69122705866507\n"
     ]
    }
   ],
   "source": [
    "print(f\"偏度{df['room_price'].skew()}\")\n",
    "print(f\"峰度{df['room_price'].kurt()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a8707ccd-dd3f-44ec-bb29-5f226d61782e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x174c7941490>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看是否是正态分布\n",
    "import seaborn as sns\n",
    "sns.displot(df['room_price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b9871a61-cc34-45ea-ad31-f50cccb58daf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 913.13 and sigma = 1463.73\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 正态分布变换\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from scipy import stats\n",
    "plt.figure(figsize = (8, 6))\n",
    "\n",
    "# 清洗数据，去除非数值和缺失值\n",
    "df['room_price'] = df['room_price'].replace([np.inf, -np.inf], np.nan).dropna()\n",
    "\n",
    "# 绘制直方图并拟合正态分布\n",
    "sns.histplot(df['room_price'], stat=\"density\", kde=True)\n",
    "(mu, sigma) = norm.fit(df['room_price'])\n",
    "\n",
    "# 输出均值和标准差\n",
    "print(f\"mu = {mu:.2f} and sigma = {sigma:.2f}\")\n",
    "\n",
    "# 如果需要在图上显示拟合的正态分布曲线，可以进一步操作（但histplot默认不直接支持fit参数）\n",
    "x = np.linspace(df['room_price'].min(), df['room_price'].max(), 100)\n",
    "plt.plot(x, norm.pdf(x, mu, sigma), 'k', linewidth=2)\n",
    "\n",
    "plt.xlabel('price')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Price Distribution with Normal Fit');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "da9edb08-efbb-42b4-8824-010a6bb44c15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu = 6.30 and sigma = 0.92\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_21720\\14334965.py:8: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['room_price'], fit = norm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对数变换log(1+x)\n",
    "# 通过对数变换将其转换为服从正态分布的变量\n",
    "df['room_price'] = np.log1p(df['room_price'])\n",
    "(mu, sigma) = norm.fit(df['room_price'])\n",
    "print('mu = {:.2f} and sigma = {:.2f}'.format(mu, sigma))\n",
    "\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.distplot(df['room_price'], fit = norm)\n",
    "plt.legend(['Normal dist. ($\\mu = $ {:.2f} and $\\sigma$ = {:.2f})'.format(mu, sigma)], loc = 'best')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Price distribution')\n",
    "\n",
    "plt.figure(figsize = (8, 6))\n",
    "stats.probplot(df['room_price'], plot = plt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d46935c-8017-4901-ba15-e6e156c6cebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='area', ylabel='room_price'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 总价和面积的关系\n",
    "plt.figure(figsize=(8,7))\n",
    "sns.scatterplot(x='area',y='room_price',data=df,s=16)\n",
    "# 面积和总价呈正相关，面积越大，价格越高。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea6c2e3-fce7-4b1c-8770-b85e2f8d89bb",
   "metadata": {},
   "source": [
    "## 三、特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfeeb631-7336-4475-ac7d-3720b47b729f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_id</th>\n",
       "      <th>room_title</th>\n",
       "      <th>room_address</th>\n",
       "      <th>hx</th>\n",
       "      <th>cz</th>\n",
       "      <th>bed</th>\n",
       "      <th>people</th>\n",
       "      <th>area</th>\n",
       "      <th>applause_rate</th>\n",
       "      <th>room_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>462542</td>\n",
       "      <td>双床房空调/步行去古城/近苍山/摄影基地场景随便拍/法式复古文艺清新婚礼花园餐桌</td>\n",
       "      <td>大理-大理市-大理古城西门村6组97号-西门村</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.298317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1089161</td>\n",
       "      <td>多肉美学花园•苍山别墅套间｜侘寂原木风吊床房｜星级配置•提供地暖•免费停车</td>\n",
       "      <td>大理-大理市-大凤路-大理的小院子南区</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.811141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1089163</td>\n",
       "      <td>多肉美学花园•苍山别墅套间｜ 北欧家庭双床房｜星级配置•提供地暖•免费停车</td>\n",
       "      <td>大理-大理市-大凤路-大理的小院子南区</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.177944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1089165</td>\n",
       "      <td>多肉美学花园•苍山别墅套间｜和风庭院套房｜星级配置•安保严密｜提供地暖•免费停车</td>\n",
       "      <td>大理-大理市-大凤路-大理的小院子南区</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.269096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264261</td>\n",
       "      <td>【韶华.青衫烟雨】大理小院子新中式别墅尊享大床房</td>\n",
       "      <td>大理-大理市-G214(西景线)-苍海一墅度假别墅</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.826000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   room_id                                room_title  \\\n",
       "0   462542  双床房空调/步行去古城/近苍山/摄影基地场景随便拍/法式复古文艺清新婚礼花园餐桌   \n",
       "1  1089161     多肉美学花园•苍山别墅套间｜侘寂原木风吊床房｜星级配置•提供地暖•免费停车   \n",
       "2  1089163     多肉美学花园•苍山别墅套间｜ 北欧家庭双床房｜星级配置•提供地暖•免费停车   \n",
       "3  1089165  多肉美学花园•苍山别墅套间｜和风庭院套房｜星级配置•安保严密｜提供地暖•免费停车   \n",
       "4   264261                  【韶华.青衫烟雨】大理小院子新中式别墅尊享大床房   \n",
       "\n",
       "                room_address  hx  cz  bed  people  area  applause_rate  \\\n",
       "0    大理-大理市-大理古城西门村6组97号-西门村   1   1    2       3  32.0           99.0   \n",
       "1        大理-大理市-大凤路-大理的小院子南区   1   1    1       2  28.0          100.0   \n",
       "2        大理-大理市-大凤路-大理的小院子南区   1   1    2       3  35.0          100.0   \n",
       "3        大理-大理市-大凤路-大理的小院子南区   1   1    1       2  56.0          100.0   \n",
       "4  大理-大理市-G214(西景线)-苍海一墅度假别墅   1   1    1       2  35.0          100.0   \n",
       "\n",
       "   room_price  \n",
       "0    5.298317  \n",
       "1    5.811141  \n",
       "2    6.177944  \n",
       "3    6.269096  \n",
       "4    5.826000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b586341d-9548-43ef-9588-4073a6a16e32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "room_id\n",
      "room_title\n",
      "room_address\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_title</th>\n",
       "      <th>room_address</th>\n",
       "      <th>hx</th>\n",
       "      <th>cz</th>\n",
       "      <th>bed</th>\n",
       "      <th>people</th>\n",
       "      <th>area</th>\n",
       "      <th>applause_rate</th>\n",
       "      <th>room_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>368</td>\n",
       "      <td>223</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5.298317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>417</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.811141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>416</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.177944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>418</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.269096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>241</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>35.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.826000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   room_title  room_address  hx  cz  bed  people  area  applause_rate  \\\n",
       "0         368           223   1   1    2       3  32.0           99.0   \n",
       "1         417           149   1   1    1       2  28.0          100.0   \n",
       "2         416           149   1   1    2       3  35.0          100.0   \n",
       "3         418           149   1   1    1       2  56.0          100.0   \n",
       "4         241            23   1   1    1       2  35.0          100.0   \n",
       "\n",
       "   room_price  \n",
       "0    5.298317  \n",
       "1    5.811141  \n",
       "2    6.177944  \n",
       "3    6.269096  \n",
       "4    5.826000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# 标签编码对不连续的分类特征进行编号，转换成连续的数值型变量\n",
    "cols = set(df.columns)-set([\"hx\",\"cz\",\"bed\",\"people\", \"area\", \"applause_rate\", \"room_price\"])\n",
    "for c in cols:\n",
    "    le = LabelEncoder()\n",
    "    le.fit(list(df[c].values))\n",
    "    print(c)\n",
    "    df[c] = le.transform(list(df[c].values))\n",
    "df.drop('room_id', axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "92317dc6-1242-4802-9bbe-e8575425bb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_title         int32\n",
       "room_address       int32\n",
       "hx                 int32\n",
       "cz                 int32\n",
       "bed                int32\n",
       "people             int32\n",
       "area             float64\n",
       "applause_rate    float64\n",
       "room_price       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "22f020c3-d045-4bbd-9e3f-0262c0abccc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_title      -0.022157\n",
       "room_address    -0.062841\n",
       "hx               0.210599\n",
       "cz               0.204312\n",
       "bed              0.562110\n",
       "people           0.572659\n",
       "area             0.618985\n",
       "applause_rate    0.024372\n",
       "room_price       1.000000\n",
       "Name: room_price, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到 'room_price' 列与其他所有列的相关系数（皮尔逊相关系数），可以帮助我们了解房价与其他各个特征变量之间的线性关联程度。\n",
    "df.corr()['room_price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "822a225b-6822-413d-8d84-b042033cdf08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_title</th>\n",
       "      <th>room_address</th>\n",
       "      <th>hx</th>\n",
       "      <th>cz</th>\n",
       "      <th>bed</th>\n",
       "      <th>people</th>\n",
       "      <th>area</th>\n",
       "      <th>applause_rate</th>\n",
       "      <th>room_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>room_title</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161465</td>\n",
       "      <td>0.062429</td>\n",
       "      <td>-0.092535</td>\n",
       "      <td>-0.053152</td>\n",
       "      <td>-0.051882</td>\n",
       "      <td>-0.070605</td>\n",
       "      <td>0.020670</td>\n",
       "      <td>-0.022157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_address</th>\n",
       "      <td>0.161465</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026157</td>\n",
       "      <td>0.045951</td>\n",
       "      <td>-0.007171</td>\n",
       "      <td>0.013331</td>\n",
       "      <td>0.006154</td>\n",
       "      <td>0.015377</td>\n",
       "      <td>-0.062841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hx</th>\n",
       "      <td>0.062429</td>\n",
       "      <td>0.026157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.498433</td>\n",
       "      <td>0.341209</td>\n",
       "      <td>0.329770</td>\n",
       "      <td>0.262857</td>\n",
       "      <td>-0.030250</td>\n",
       "      <td>0.210599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cz</th>\n",
       "      <td>-0.092535</td>\n",
       "      <td>0.045951</td>\n",
       "      <td>-0.498433</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362284</td>\n",
       "      <td>0.391267</td>\n",
       "      <td>0.370224</td>\n",
       "      <td>0.002559</td>\n",
       "      <td>0.204312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed</th>\n",
       "      <td>-0.053152</td>\n",
       "      <td>-0.007171</td>\n",
       "      <td>0.341209</td>\n",
       "      <td>0.362284</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.953643</td>\n",
       "      <td>0.782445</td>\n",
       "      <td>-0.048736</td>\n",
       "      <td>0.562110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>people</th>\n",
       "      <td>-0.051882</td>\n",
       "      <td>0.013331</td>\n",
       "      <td>0.329770</td>\n",
       "      <td>0.391267</td>\n",
       "      <td>0.953643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.798883</td>\n",
       "      <td>-0.039337</td>\n",
       "      <td>0.572659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>-0.070605</td>\n",
       "      <td>0.006154</td>\n",
       "      <td>0.262857</td>\n",
       "      <td>0.370224</td>\n",
       "      <td>0.782445</td>\n",
       "      <td>0.798883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020527</td>\n",
       "      <td>0.618985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>applause_rate</th>\n",
       "      <td>0.020670</td>\n",
       "      <td>0.015377</td>\n",
       "      <td>-0.030250</td>\n",
       "      <td>0.002559</td>\n",
       "      <td>-0.048736</td>\n",
       "      <td>-0.039337</td>\n",
       "      <td>-0.020527</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_price</th>\n",
       "      <td>-0.022157</td>\n",
       "      <td>-0.062841</td>\n",
       "      <td>0.210599</td>\n",
       "      <td>0.204312</td>\n",
       "      <td>0.562110</td>\n",
       "      <td>0.572659</td>\n",
       "      <td>0.618985</td>\n",
       "      <td>0.024372</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               room_title  room_address        hx        cz       bed  \\\n",
       "room_title       1.000000      0.161465  0.062429 -0.092535 -0.053152   \n",
       "room_address     0.161465      1.000000  0.026157  0.045951 -0.007171   \n",
       "hx               0.062429      0.026157  1.000000 -0.498433  0.341209   \n",
       "cz              -0.092535      0.045951 -0.498433  1.000000  0.362284   \n",
       "bed             -0.053152     -0.007171  0.341209  0.362284  1.000000   \n",
       "people          -0.051882      0.013331  0.329770  0.391267  0.953643   \n",
       "area            -0.070605      0.006154  0.262857  0.370224  0.782445   \n",
       "applause_rate    0.020670      0.015377 -0.030250  0.002559 -0.048736   \n",
       "room_price      -0.022157     -0.062841  0.210599  0.204312  0.562110   \n",
       "\n",
       "                 people      area  applause_rate  room_price  \n",
       "room_title    -0.051882 -0.070605       0.020670   -0.022157  \n",
       "room_address   0.013331  0.006154       0.015377   -0.062841  \n",
       "hx             0.329770  0.262857      -0.030250    0.210599  \n",
       "cz             0.391267  0.370224       0.002559    0.204312  \n",
       "bed            0.953643  0.782445      -0.048736    0.562110  \n",
       "people         1.000000  0.798883      -0.039337    0.572659  \n",
       "area           0.798883  1.000000      -0.020527    0.618985  \n",
       "applause_rate -0.039337 -0.020527       1.000000    0.024372  \n",
       "room_price     0.572659  0.618985       0.024372    1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将计算得到的相关系数矩阵存储在新的DataFrame变量 corrmat 中。\n",
    "corrmat = df.corr()\n",
    "corrmat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0c5943-ef06-4859-9742-2e4981ce1807",
   "metadata": {},
   "source": [
    "简单的相关系数的分类：\n",
    "- 0.8-1.0 极强相关\n",
    "- 0.6-0.8 强相关\n",
    "- 0.4-0.6 中等程度相关\n",
    "- 0.2-0.4 弱相关\n",
    "- 0.0-0.2 极弱相关或无相关"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "915b7d8e-b095-41a3-ad1b-24282e451334",
   "metadata": {},
   "source": [
    "使用 seaborn 库绘制热力图（heatmap）来可视化 DataFrame corrmat 中的数值数据，其中包含的是各列之间的相关系数矩阵。各个参数的含义如下：\n",
    "\n",
    "- sns.heatmap(corrmat, ...): 调用 seaborn 库中的 heatmap 函数，传入的数据为 corrmat，这是一个计算好的相关系数矩阵。\n",
    "\n",
    "- annot=True: 这个参数指明在每个单元格中显示具体的数值（即相关系数）。当设置为True时，会在每个格子中直接标注出对应的相关系数值。\n",
    "\n",
    "- square=True: 该参数使得热力图的行和列具有相同的宽度，形成一个正方形网格，便于比较对角线两侧的相关关系。\n",
    "\n",
    "- fmt='.2f': 设置注释（也就是相关系数值）的格式，'.2f'表示将浮点数保留两位小数进行显示。\n",
    "\n",
    "- cmap='PuBuGn': 指定使用的颜色映射方案（colormap），在这个例子中选择的是 \"PuBuGn\"，这是一种从紫色过渡到蓝色再到绿色的颜色渐变，用于反映相关系数的强度，通常负相关性会显示为冷色调（蓝绿），正相关性会显示为暖色调（紫红）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8c406d9e-e483-49a1-af70-488bfa3db6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征相关性可视化\n",
    "plt.subplots(figsize = (12,8))\n",
    "sns.heatmap(corrmat, annot = True, square=True,fmt = '.2f', cmap = \"PuBuGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0d9926bf-c64f-46fd-bbe1-b2ed1bb3385c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_title      -0.022157\n",
       "room_address    -0.062841\n",
       "hx               0.210599\n",
       "cz               0.204312\n",
       "bed              0.562110\n",
       "people           0.572659\n",
       "area             0.618985\n",
       "applause_rate    0.024372\n",
       "room_price       1.000000\n",
       "Name: room_price, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从 corrmat DataFrame 中选择名为 'room_price' 的行。这将返回一个Series对象，其中包含了 'room_price' 列与其他所有列的相关系数。\n",
    "corrmat['room_price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "794cb4c8-aa6e-4339-95f5-92aa1159c234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_title       0.022157\n",
       "room_address     0.062841\n",
       "hx               0.210599\n",
       "cz               0.204312\n",
       "bed              0.562110\n",
       "people           0.572659\n",
       "area             0.618985\n",
       "applause_rate    0.024372\n",
       "room_price       1.000000\n",
       "Name: room_price, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 变量相关系数的绝对值用于突出显示 'room_price' 变量与各变量间的关联强度，而不考虑关联的方向是正相关还是负相关。\n",
    "corrmat['room_price'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c21b27d3-5bd8-4a81-95e5-f1e0f7c56886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "room_price       1.000000\n",
       "area             0.618985\n",
       "people           0.572659\n",
       "bed              0.562110\n",
       "hx               0.210599\n",
       "cz               0.204312\n",
       "room_address    -0.062841\n",
       "applause_rate    0.024372\n",
       "room_title      -0.022157\n",
       "Name: room_price, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列出于房间价格相关性绝对值最高的前10个变量。\n",
    "corrmat.iloc[corrmat['room_price'].abs().argsort()[::-1]]['room_price'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e593b1ac-99c8-40af-a857-7fc13fbce811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['room_price', 'area', 'people', 'bed', 'hx', 'cz', 'room_address',\n",
       "       'applause_rate', 'room_title'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = corrmat.iloc[corrmat['room_price'].abs().argsort()[::-1]]['room_price'][:10].index\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "60f1e0fe-f600-4615-8464-2800907f4366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.61898497,  0.57265949,  0.56210954,  0.21059933,\n",
       "         0.20431215, -0.06284058,  0.02437215, -0.02215692],\n",
       "       [ 0.61898497,  1.        ,  0.79888332,  0.78244477,  0.26285698,\n",
       "         0.37022402,  0.00615365, -0.02052694, -0.07060461],\n",
       "       [ 0.57265949,  0.79888332,  1.        ,  0.95364342,  0.32976971,\n",
       "         0.39126708,  0.01333084, -0.03933691, -0.05188193],\n",
       "       [ 0.56210954,  0.78244477,  0.95364342,  1.        ,  0.34120943,\n",
       "         0.36228393, -0.00717125, -0.04873556, -0.05315203],\n",
       "       [ 0.21059933,  0.26285698,  0.32976971,  0.34120943,  1.        ,\n",
       "        -0.49843335,  0.0261572 , -0.03025007,  0.06242863],\n",
       "       [ 0.20431215,  0.37022402,  0.39126708,  0.36228393, -0.49843335,\n",
       "         1.        ,  0.04595129,  0.00255854, -0.09253505],\n",
       "       [-0.06284058,  0.00615365,  0.01333084, -0.00717125,  0.0261572 ,\n",
       "         0.04595129,  1.        ,  0.01537669,  0.16146541],\n",
       "       [ 0.02437215, -0.02052694, -0.03933691, -0.04873556, -0.03025007,\n",
       "         0.00255854,  0.01537669,  1.        ,  0.02066967],\n",
       "       [-0.02215692, -0.07060461, -0.05188193, -0.05315203,  0.06242863,\n",
       "        -0.09253505,  0.16146541,  0.02066967,  1.        ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = np.corrcoef(df[cols].values.T)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5e2210d2-457e-44a4-a98b-cc2084527be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "sns.heatmap(cm, cbar = True, annot = True, square = True, fmt = '.3f', cmap = 'Blues',\n",
    "            annot_kws = {'size': 10}, yticklabels = cols.values, xticklabels = cols.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25fc7e49-760d-46e9-b25a-987c24f52b31",
   "metadata": {},
   "source": [
    "经过上述分析，得出对price影响程度，从大到小依次为：\n",
    "- room_price       1.000000\n",
    "- area             0.618985\n",
    "- people           0.572659\n",
    "- bed              0.562110\n",
    "- hx               0.210599\n",
    "- cz               0.204312\n",
    "- room_address    -0.062841\n",
    "- applause_rate    0.024372\n",
    "- room_title      -0.022157"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad519e5e-da13-4f7f-8ea3-1f19210070ae",
   "metadata": {},
   "source": [
    "### 四、建立模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3f28b3-74c0-4eaf-bb2f-81c07f7f7a4b",
   "metadata": {},
   "source": [
    "根据特征预测民宿房价\n",
    "- LASSO回归筛选变量\n",
    "- 决策树\n",
    "- 随机森林\n",
    "- XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "af8e9721-67b1-4490-8956-bfa7d8cb838e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"area\", \"people\", \"bed\", \"hx\", \"cz\", \"room_address\", \"applause_rate\"]]\n",
    "# X = df[[\"area\", \"people\", \"bed\"]]\n",
    "y = df['room_price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a8ad01e6-ac42-4036-a7a4-028880ed9e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分数据集\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 按照默认比例（通常是75%的数据用于训练，25%的数据用于测试）\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0d50361e-d9e6-4221-883e-6fd6b234cdc9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>people</th>\n",
       "      <th>bed</th>\n",
       "      <th>hx</th>\n",
       "      <th>cz</th>\n",
       "      <th>room_address</th>\n",
       "      <th>applause_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>46.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>528</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>144.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>680</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>177.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>120.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>451</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>60.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>310</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>90.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>234</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>70.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>38.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>632</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1009 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       area  people  bed  hx  cz  room_address  applause_rate\n",
       "295    46.0       2    1   5   0           528          100.0\n",
       "756   144.0       6    3   3   1            80          100.0\n",
       "408    15.0       2    1   1   1           680          100.0\n",
       "867   177.0       5    3   3   1           588          100.0\n",
       "810   120.0       6    3   3   1           451          100.0\n",
       "...     ...     ...  ...  ..  ..           ...            ...\n",
       "1229   60.0       2    1   7   0           310          100.0\n",
       "659    90.0       4    2   2   1           234          100.0\n",
       "579    70.0       6    4   2   1           116          100.0\n",
       "392    35.0       2    1   1   1             6          100.0\n",
       "1045   38.0       2    1   4   0           632          100.0\n",
       "\n",
       "[1009 rows x 7 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea495f0f-3c1a-4dbb-9472-105326c242cb",
   "metadata": {},
   "source": [
    "#### 算法1：套索回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4399ec8c-5bf6-4e08-bfb6-b67986cdd04e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.41\n",
      "测试集得分：0.38\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "# 套索回归\n",
    "la = Lasso(alpha=0.1,max_iter=100000)\n",
    "la.fit(X_train,y_train)\n",
    "la_R2_train = la.score(X_train,y_train)\n",
    "la_R2_test = la.score(X_test,y_test)\n",
    "print(f'训练集得分：{round(la_R2_train,2)}')\n",
    "print(f'测试集得分：{round(la_R2_test,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "289c11b3-d8d8-4c73-bbc9-a8e2780563c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.05\n",
      "测试集得分：0.34\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "# 计算模型的均方误差\n",
    "la_mse_train = mean_squared_error(y_train, la.predict(X_train))\n",
    "la_mse_test = mean_squared_error(y_test, la.predict(X_test))\n",
    "print(f'训练集得分：{round(mse_train,2)}')\n",
    "print(f'测试集得分：{round(mse_test,2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb77cfa-62b6-433d-9aee-84dc0fd48443",
   "metadata": {},
   "source": [
    "#### 算法2：决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "607bdffc-84e1-4e24-b467-bf8316ac0424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.65\n",
      "测试集得分：0.5\n"
     ]
    }
   ],
   "source": [
    "# 决策树\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt = DecisionTreeRegressor(max_depth = 6)\n",
    "dt.fit(X_train,y_train)\n",
    "dt_R2_train = dt.score(X_train,y_train)\n",
    "dt_R2_test = dt.score(X_test,y_test)\n",
    "print(f'训练集得分：{round(dt_R2_train,2)}')\n",
    "print(f'测试集得分：{round(dt_R2_test,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7cd70294-6470-41c4-8ceb-579a008f3f62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.29\n",
      "测试集得分：0.43\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "# 计算模型的均方误差\n",
    "dt_mse_train = mean_squared_error(y_train, dt.predict(X_train))\n",
    "dt_mse_test = mean_squared_error(y_test, dt.predict(X_test))\n",
    "print(f'训练集得分：{round(dt_mse_train,2)}')\n",
    "print(f'测试集得分：{round(dt_mse_test,2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb8e95f-54be-41dd-a8e6-9520393513f6",
   "metadata": {},
   "source": [
    "#### 算法3：xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "64ba3e11-17bd-4287-b4ac-c0711f05f844",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.98\n",
      "测试集得分：0.62\n"
     ]
    }
   ],
   "source": [
    "# xgboost\n",
    "from xgboost import XGBRegressor\n",
    "xgbr = XGBRegressor()\n",
    "xgbr.fit(X_train,y_train)\n",
    "xgbr_R2_train = xgbr.score(X_train,y_train)\n",
    "xgbr_R2_test = xgbr.score(X_test,y_test)\n",
    "print(f'训练集得分：{round(xgbr_R2_train,2)}')\n",
    "print(f'测试集得分：{round(xgbr_R2_test,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5efbfc07-57ca-4326-958c-159642005a6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.02\n",
      "测试集得分：0.32\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "# 计算模型的均方误差\n",
    "xgbr_mse_train = mean_squared_error(y_train, xgbr.predict(X_train))\n",
    "xgbr_mse_test = mean_squared_error(y_test, xgbr.predict(X_test))\n",
    "print(f'训练集得分：{round(xgbr_mse_train,2)}')\n",
    "print(f'测试集得分：{round(xgbr_mse_test,2)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2243496-1a0d-46d7-92f1-7689768eaff3",
   "metadata": {},
   "source": [
    "#### 算法4：随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b3e5e9cc-490a-4a6d-bdbe-a5dfa3e74ca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.95\n",
      "测试集得分：0.6\n"
     ]
    }
   ],
   "source": [
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestRegressor # ensemble learning: 集成学习\n",
    "rfr = RandomForestRegressor()\n",
    "rfr.fit(X_train,y_train)\n",
    "rfr_R2_train = rfr.score(X_train,y_train)\n",
    "rfr_R2_test = rfr.score(X_test,y_test)\n",
    "print(f'训练集得分：{round(rfr_R2_train,2)}')\n",
    "print(f'测试集得分：{round(rfr_R2_test,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3c3ef3b4-93e9-4835-86b8-f9153c6acccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集得分：0.05\n",
      "测试集得分：0.34\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "# 计算模型的均方误差\n",
    "rfr_mse_train = mean_squared_error(y_train, rfr.predict(X_train))\n",
    "rfr_mse_test = mean_squared_error(y_test, rfr.predict(X_test))\n",
    "print(f'训练集得分：{round(rfr_mse_train,2)}')\n",
    "print(f'测试集得分：{round(rfr_mse_test,2)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "dc44f3ee-cf92-4641-bffd-d8676e1258da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['font.sans-serif'] = ['SimHei']  # 设置字体为SimHei显示中文\n",
    "rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "species = (\"Lasso\", \"决策树\", \"xgboost\", \"随机森林\")  # 修改坐标轴标签\n",
    "penguin_means = {\n",
    "    '训练集得分': (round(la_R2_train, 2), round(dt_R2_train, 2), round(xgbr_R2_train, 2), round(rfr_R2_train,2)),\n",
    "    '测试集得分': (round(la_R2_test, 2), round(dt_R2_test, 2), round(xgbr_R2_test, 2), round(rfr_R2_test, 2)),\n",
    "}\n",
    "\n",
    "x = np.arange(len(species))  # the label locations\n",
    "width = 0.20  # the width of the bars, adjusted for 4 groups\n",
    "multiplier = 0\n",
    "\n",
    "fig, ax = plt.subplots(layout='constrained')\n",
    "\n",
    "for attribute, measurement in penguin_means.items():\n",
    "    offset = width * multiplier\n",
    "    rects = ax.bar(x + offset, measurement, width, label=attribute)\n",
    "    ax.bar_label(rects, padding=3)\n",
    "    multiplier += 1\n",
    "\n",
    "ax.set_ylabel('决定系数得分')\n",
    "ax.set_title('模型评估')\n",
    "ax.set_xticks(x + width, species)  # 设置x轴标签\n",
    "ax.set_xticklabels(species)  # 确保使用新的标签\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1, 1))  # 为图例找一个合适的位置\n",
    "ax.set_ylim(0, 1.2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9730d59b-7d0c-4efa-8833-ac2f06cd6c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['font.sans-serif'] = ['SimHei']  # 设置字体为SimHei显示中文\n",
    "rcParams['axes.unicode_minus'] = False  # 解决负号'-'显示为方块的问题\n",
    "\n",
    "species = (\"Lasso\", \"决策树\", \"xgboost\", \"随机森林\")  # 修改坐标轴标签\n",
    "penguin_means = {\n",
    "    '训练集得分': (round(la_mse_train, 2), round(dt_mse_train, 2), round(xgbr_mse_train, 2), round(rfr_mse_train, 2)),\n",
    "    '测试集得分': (round(la_mse_test, 2), round(dt_mse_test, 2), round(xgbr_mse_test, 2), round(rfr_mse_test, 2)),\n",
    "}\n",
    "\n",
    "x = np.arange(len(species))  # the label locations\n",
    "width = 0.20  # the width of the bars, adjusted for 4 groups\n",
    "multiplier = 0\n",
    "\n",
    "fig, ax = plt.subplots(layout='constrained')\n",
    "\n",
    "for attribute, measurement in penguin_means.items():\n",
    "    offset = width * multiplier\n",
    "    rects = ax.bar(x + offset, measurement, width, label=attribute)\n",
    "    ax.bar_label(rects, padding=3)\n",
    "    multiplier += 1\n",
    "\n",
    "ax.set_ylabel('决定系数得分')\n",
    "ax.set_title('模型评估')\n",
    "ax.set_xticks(x + width, species)  # 设置x轴标签\n",
    "ax.set_xticklabels(species)  # 确保使用新的标签\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1, 1))  # 为图例找一个合适的位置\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "407cdc97-94df-47f6-bf7f-558f8df2fba4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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